The development of Information Communication Technologies (ICTs) has dramatically changed the way of hotel booking. The increasing number of online consumers provides abundant data for demand forcasting in revenue management. The current methods e.g. historical data analysis, normally focus on studying consumers’ behaviors and preferences in the hotel but may not be able to integrate that out of the hotel such as dining, travelling, shopping and entertaining, which will bring crucial references to the co-relationship between these consumption and the way of hotel booking. This research adopts a persona approach to infer consumers’ preference probability by using a variety of real-time data. Quantitative methods are employed. In order to predict the booking needs accurately, this research establish a Bayesian network on the online platform for life service that can be associated with various consumer behavior data. The results indicate that in the environment of uncertain demand, the algorithm is effective and applicable, which will help directors of hotel revenue management in accurate price planning and decision making.

In reality, distribution planning for a supply chain is established using a certain probabilistic distribution estimated by forecasting. However, in general, the demands used for an actual distribution planning are of deterministic value, a single value for each of periods. Because of this reason the final result of a planning has to be a single value for each period. Unfortunately, it is very difficult to estimate a single value due to the inherent uncertainty in the probabilistic distribution of customer demand. The issue addressed in this paper is the selection of single demand value among of the distributed demand estimations for a period to be used in the distribution planning. This paper proposes an efficient demand selection scheme for minimizing total inventory costs while satisfying target service level under the various experimental conditions.